Topic bridging determination using topical graphs

ABSTRACT

One embodiment provides a method that includes obtaining information including profile information and current event information. A processor generates a topic graph by converting the information to topic nodes in the topic graph. The processor determines a weight assignment for each topic node based on ratios of sums of weights of edges from topic nodes. Bridges are provided from a given topic node to a neighbor based on the weight assignment.

BACKGROUND

Text mining may be used to extract information from documents and smallpieces of documents called snippets. A snippet can be definedgrammatically (e.g., sentence or paragraph) or it can be defined by anumber of words. In conversations between an individual and a computer,the computer typically reacts to the individual using speechrecognition. This reaction from the computer involves the informationprovided by the individual, such as problem solving or making areservation that is driven from the individual's speech.

SUMMARY

Embodiments relate to topic bridging determination using topical graphs.One embodiment provides a method that includes obtaining informationdata including profile information and current event information. Aprocessor generates a topic graph by converting the information data totopic nodes in the topic graph. The processor determines a weightassignment for each topic node based on ratios of sums of weights ofedges from topic nodes. Bridges are provided from a given topic node toa neighbor based on the weight assignment.

These and other features, aspects and advantages of the presentinvention will become understood with reference to the followingdescription, appended claims and accompanying figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a cloud computing environment, according to anembodiment;

FIG. 2 depicts a set of abstraction model layers, according to anembodiment;

FIG. 3 is a network architecture for retrospective snapshots inlog-structured storage systems, according to an embodiment;

FIG. 4 shows a representative hardware environment that may beassociated with the servers and/or clients of FIG. 1, according to anembodiment;

FIG. 5 is a block diagram illustrating a processor implemented for topicbridging determination using topic graphs, according to an embodiment;

FIG. 6 illustrates an example generated topic graph showing edgesbetween topic nodes, according to an embodiment;

FIG. 7 is a block diagram illustrating an example flow for bridgingtopics to a goal topic using a generated topic graph, according to anembodiment;

FIG. 8 is a block diagram illustrating an example flow for a firstbreadth-first search, according to an embodiment;

FIG. 9 is a block diagram illustrating an example flow for a secondbreadth-first search, according to an embodiment;

FIG. 10 is a block diagram illustrating another example flow forbridging topics to a goal topic using a generated topic graph, accordingto an embodiment;

FIGS. 11-15 are examples of adding weights on nodes for bridging topicsto a goal topic, according to an embodiment;

FIGS. 16A-B shows an example of interaction between a client and asalesperson using an embodiment for bridging topics to a goal topicusing bridges of a topic graph, according to an embodiment; and

FIG. 17 illustrates a block diagram for a process for topic bridgingdetermination using topic graphs, according to one embodiment.

DETAILED DESCRIPTION

The descriptions of the various embodiments have been presented forpurposes of illustration, but are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationswill be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments. The terminologyused herein was chosen to best explain the principles of theembodiments, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

It is understood in advance that although this disclosure includes adetailed description of cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

One or more embodiments provide for topic bridging determination usingtopical graphs for guiding computer automated conversations with apotential client or for selection of conversational bridges (i.e.,segues) for representative conversations with a potential client. Oneembodiment includes obtaining information data including profileinformation and current event information. A processor generates a topicgraph by converting the information data to topic nodes in the topicgraph. The processor determines a weight assignment for each topic nodebased on ratios of sums of weights of edges from topic nodes. Bridgesare provided from a given topic node to a neighbor based on the weightassignment.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines (VMs), and services)that can be rapidly provisioned and released with minimal managementeffort or interaction with a provider of the service. This cloud modelmay include at least five characteristics, at least three servicemodels, and at least four deployment models.

Characteristics are as Follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded and automatically, without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneous,thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or data center).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned and, in some cases, automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active consumer accounts). Resource usage canbe monitored, controlled, and reported, thereby providing transparencyfor both the provider and consumer of the utilized service.

Service Models are as Follows:

Software as a Service (SaaS): the capability provided to the consumer isthe ability to use the provider's applications running on a cloudinfrastructure. The applications are accessible from various clientdevices through a thin client interface, such as a web browser (e.g.,web-based email). The consumer does not manage or control the underlyingcloud infrastructure including network, servers, operating systems,storage, or even individual application capabilities, with the possibleexception of limited consumer-specific application configurationsettings.

Platform as a Service (PaaS): the capability provided to the consumer isthe ability to deploy onto the cloud infrastructure consumer-created oracquired applications created using programming languages and toolssupported by the provider. The consumer does not manage or control theunderlying cloud infrastructure including networks, servers, operatingsystems, or storage, but has control over the deployed applications andpossibly application-hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is the ability to provision processing, storage, networks, andother fundamental computing resources where the consumer is able todeploy and run arbitrary software, which can include operating systemsand applications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as Follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting for loadbalancing between clouds).

A cloud computing environment is a service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, an illustrative cloud computing environment 50is depicted. As shown, cloud computing environment 50 comprises one ormore cloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as private, community,public, or hybrid clouds as described hereinabove, or a combinationthereof. This allows the cloud computing environment 50 to offerinfrastructure, platforms, and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 2, a set of functional abstraction layers providedby the cloud computing environment 50 (FIG. 1) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 2 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, a management layer 80 may provide the functionsdescribed below. Resource provisioning 81 provides dynamic procurementof computing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and conversational bridging processing 96. Asmentioned above, all of the foregoing examples described with respect toFIG. 2 are illustrative only, and the invention is not limited to theseexamples.

It is understood all functions of one or more embodiments as describedherein may be typically performed by the processing system 300 (FIG. 3)or the processor 500 (FIG. 5), which can be tangibly embodied ashardware processors and with modules of program code. However, this neednot be the case for non-real-time processing. Rather, for non-real-timeprocessing the functionality recited herein could be carriedout/implemented and/or enabled by any of the layers 60, 70, 80 and 90shown in FIG. 2.

It is reiterated that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather, theembodiments of the present invention may be implemented with any type ofclustered computing environment now known or later developed.

FIG. 3 illustrates a network architecture 300, in accordance with oneembodiment. As shown in FIG. 3, a plurality of remote networks 302 areprovided, including a first remote network 304 and a second remotenetwork 306. A gateway 301 may be coupled between the remote networks302 and a proximate network 308. In the context of the present networkarchitecture 300, the networks 304, 306 may each take any formincluding, but not limited to, a LAN, a WAN, such as the Internet,public switched telephone network (PSTN), internal telephone network,etc.

In use, the gateway 301 serves as an entrance point from the remotenetworks 302 to the proximate network 308. As such, the gateway 301 mayfunction as a router, which is capable of directing a given packet ofdata that arrives at the gateway 301, and a switch, which furnishes theactual path in and out of the gateway 301 for a given packet.

Further included is at least one data server 314 coupled to theproximate network 308, which is accessible from the remote networks 302via the gateway 301. It should be noted that the data server(s) 314 mayinclude any type of computing device/groupware. Coupled to each dataserver 314 is a plurality of user devices 316. Such user devices 316 mayinclude a desktop computer, laptop computer, handheld computer, printer,and/or any other type of logic-containing device. It should be notedthat a user device 311 may also be directly coupled to any of thenetworks in some embodiments.

A peripheral 320 or series of peripherals 320, e.g., facsimile machines,printers, scanners, hard disk drives, networked and/or local storageunits or systems, etc., may be coupled to one or more of the networks304, 306, 308. It should be noted that databases and/or additionalcomponents may be utilized with, or integrated into, any type of networkelement coupled to the networks 304, 306, 308. In the context of thepresent description, a network element may refer to any component of anetwork.

According to some approaches, methods and systems described herein maybe implemented with and/or on virtual systems and/or systems, whichemulate one or more other systems, such as a UNIX system that emulatesan IBM z/OS environment, a UNIX system that virtually hosts a MICROSOFTWINDOWS environment, a MICROSOFT WINDOWS system that emulates an IBMz/OS environment, etc. This virtualization and/or emulation may beimplemented through the use of VMWARE software in some embodiments.

FIG. 4 shows a representative hardware system 400 environment associatedwith a user device 416 and/or server 314 of FIG. 3, in accordance withone embodiment. In one example, a hardware configuration includes aworkstation having a central processing unit 410, such as amicroprocessor, and a number of other units interconnected via a systembus 412. The workstation shown in FIG. 4 may include a Random AccessMemory (RAM) 414, Read Only Memory (ROM) 416, an I/O adapter 418 forconnecting peripheral devices, such as disk storage units 420 to the bus412, a user interface adapter 422 for connecting a keyboard 424, a mouse426, a speaker 428, a microphone 432, and/or other user interfacedevices, such as a touch screen, a digital camera (not shown), etc., tothe bus 412, communication adapter 434 for connecting the workstation toa communication network 435 (e.g., a data processing network) and adisplay adapter 436 for connecting the bus 412 to a display device 438.

In one example, the workstation may have resident thereon an operatingsystem, such as the MICROSOFT WINDOWS Operating System (OS), a MAC OS, aUNIX OS, etc. In one embodiment, the system 400 employs a POSIX® basedfile system. It will be appreciated that other examples may also beimplemented on platforms and operating systems other than thosementioned. Such other examples may include operating systems writtenusing JAVA, XML, C, and/or C++ language, or other programming languages,along with an object oriented programming methodology. Object orientedprogramming (OOP), which has become increasingly used to develop complexapplications, may also be used.

FIG. 5 is a block diagram illustrating a processing node 500 for topicbridging determination using topic graphs, according to an embodiment.The processing node 500 includes one or more processors 510, a segueinterface 530 and a memory 520. In one embodiment, each processor(s) 510performs processing for generation of topic graphs includinginformation, such as personal information of a potential client,individual or entity, proper nouns, current events, individual currentevents (e.g., health events, etc.), product categories, etc. The segueinterface 530 provides traversal of the generated topic graphs where theedges represent appearance in a same snippet, which can be definedgrammatically (e.g., sentence or paragraph) or as a particular number ofwords. In one example, a “tweet” can be treated as a snippet. The segueinterface 530 determines conversational bridges between two topics whereone participant in the conversation is guiding the conversation and theother (e.g., a programmed computer or process acting as a virtualrepresentative using synthesized speech or text) is responding andchoosing new topics independently. In one example, the segue interface530 may be used in a sales conversation between a seller (e.g., aprogrammed computer or process acting as a sales representative usingsynthesized speech or text or a user of an application that guidesconversational bridges between topics and provides recommendations(e.g., using text on a display, synthesized speech through a listeningdevice (e.g., an ear bud, earphones, etc.))) and a potential client.

In one embodiment, the segue interface 530 obtains or receivesinformation about a potential client or customer and information about aproduct or service that a salesperson or computerized representative isattempting to sell. In one example, the information may be obtained bycustomer/client profiles, information scraped from networks (e.g., theInternet or local area networks (LANs)), information manually enteredinto a database or application, provided by a third party, etc. In oneembodiment, the segue interface 530 determines and providesrecommendations to the sales person or computerized representativeprocess in the form of conversational bridges that are likely to resultin increasing customer interest to the extent that the customerexperiences the product or service in question as the idea of thecustomer, or at least a joint product of the conversation.

In one embodiment, the segue interface 530 defines and providesheuristic solutions to the problem of selecting segues (conversationalbridges) that increase the probability that the conversation movestoward a specified topic goal (or goals), making use of information suchas current news, current events, hot topics and other mutuallyaccessible topics of conversation. Some example use cases that may usesuggested or selected conversational topics leading to a goal topic maybe sales conversations with a potential customer, interviews (e.g.,attempting to obtain an answer for a specific goal topic), etc.

In one embodiment, the memory 520 stores instructions and the processor510 executes the instructions by communicating with the segue interface530 to: obtain information including profile information and currentevent information, generate a topic graph by converting the informationto topic nodes in the topic graph, determine distance for each of thetopic nodes in the topic graph to a topic goal node based on a fractionof neighbors of each topic node that are closer by edge count orweighted edge count to the topic goal node than the topic node, along apath to the topic goal node, and provide suggested paths from neighbortopic nodes to the topic goal node based on determined distance betweeneach of the topic nodes to the topic goal node.

FIG. 6 illustrates an example generated topic graph 600 showing edgesbetween topic nodes, according to an embodiment. The topic graphincludes information, such as personal information of a potentialclient, individual or entity, proper nouns, current events, individualcurrent events (e.g., health events, etc.), product categories, etc. Inone example, the profile of a customer (e.g., topic node C 610) providesedges (E¹ to E^(N)) to various topic nodes (e.g., topic w¹). Currentevents provide both topics and edges. The processing node 500 determinesa path for a conversationally appropriate way to navigate from C to thedesired topic node goal P 630. In the example topical graph 600, thedesired example path is path 620, which has the minimal number of edges(e.g., E^(VI) and E^(VIII)).

In one embodiment, a topic graph is maintained based on personal (orclient entity) profiles, product and service profiles, and currentevents (e.g., news, historical events, emergency events, weather relatedevents, sporting events, personal health events, etc.). The topics mayalso include known synonyms. In one embodiment, the profiles may beobtained from individuals, entities, etc., received from third parties,etc. Given a potential customer, a salesperson (e.g., a virtualrepresentative or real person), and a product (or service), the topicgraph is restricted to topics connected to the customer, thesalesperson, or the product/service by means of snippets fromdescriptions of current events. From the goal (product) node in thetarget topic graph, the processing node 500 (FIG. 5) performs a variantof breadth-first search labeling each node visited with a derivedconversational distance to the goal. (Note that this distance is not asymmetric distance measure on the topic graph). The processing nodeprovides a recommendation to the salesperson (or virtual representativeprocess) the path involving shortest distance to the goal topic from thepotential customer node, with advantage going to the path with firstnode shared by both potential customer and salesperson in case of ties.In one example, the goal of the salesperson is to try to keep theconversation on the recommended path. The path is chosen so that thecustomer is also likely to choose a conversational topic that stays onthe path or can be easily returned to the path.

FIG. 7 is a block diagram illustrating an example flow 700 for bridgingtopics to a goal topic using a generated topic graph, according to anembodiment. As shown, the example flow 700 includes topics, instances ofthe topic and news. In one embodiment, the connectors between thetopics, instances of topics and news are classified as “is a” or “has,”and bridges to get to the goal topic (e.g., a product). In this exampleflow 700, John Smith “is a” client and “has” an interest in tennis, golfand kids. In the example flow 700, tennis “has a” a real-life event ofpublic interests is related to the instance of topic: tennis. Newstopics are provided, for example, from an analytic service or server(e.g., IBM analytics). The instance of topic Wimbledon 2012 has relatednews and is a bridge to the instance of topic U.S. Open, which in turnis a bridge to an instance of topic IBM Analytics, which is a product tosell that is the goal for segueing the conversation.

Returning to FIG. 5, the processing node 500 uses the processor 510,memory 520 and segue interface 530 to perform the following processing.In the generated topic graph represented in the memory 520, the segueinterface begins a breadth-first search from the topic goal node.

FIG. 8 is a block diagram illustrating an example flow 800 for a firstbreadth-first search, according to an embodiment in which labels calledN-labels are provided for nodes. In block 810 each goal node is labeled0 (e.g., N (node)=0) and a frontier (0)=goal starting out where X=0(where X is the iteration). In block 820 it is determined whether thereare unlabeled (with N-label) neighbors of frontier (X). If it isdetermined that there are neighbors of frontier (X), then the flow 800proceeds to block 830, otherwise the flow 800 proceeds to block 840. Inblock 840, X is set to X+1 and frontier (X) is set equal to neighborswithout N-labels of the previous frontier nodes.

In block 830, for each node of frontier (X), the N-label is set equal tothe ratio of the total number of edges from the node to the number ofedges to a node in frontier (Y) with Y≤X. In block 850 it is determinedwhether there are any more reachable nodes that are not N-labeled. If itis determined that there are more reachable nodes that are notN-labeled, flow 800 proceeds to block 820, otherwise the flow iscompleted (i.e., end of the first breadth-first-search) at block 860 andthe next search may begin (flow 900, FIG. 9).

FIG. 9 is a block diagram illustrating an example flow 900 for a secondbreadth-first search, according to an embodiment. In block 910 each goalnode is D-labeled 0 (e.g., D (node)=0) and a frontier (0)=goal startingout where X=0 (where X is the iteration) and D is a measure of distanceto the goal set of nodes. In block 920 it is determined whether thereare unlabeled (with label D) neighbors of frontier (X). If it isdetermined that there are neighbors of frontier (X), then the flow 900proceeds to block 940, otherwise the flow 900 proceeds to block 930. Inblock 930, X is set to X+1 and frontier (X) is set equal to theneighbors of the previous frontier without D-labels (Y and X areintegers).

In block 940, for each node without a D-label of frontier (X), D (node)is set equal to the minimum sum (N (node[i], i=1 . . . m)) wherenode[i], i=1 . . . m is a path from neighbor to goal. In block 950 it isdetermined whether there are any more reachable nodes that are notD-labeled. If it is determined that there are more reachable nodes thatare not D-labeled, flow 900 proceeds to block 920, otherwise the flow900 is completed (i.e., end of the second breadth-first-search).

FIG. 10 is a block diagram illustrating an example flow for bridgingtopics to a goal topic using a generated topical graph, according to anembodiment. In this example, a client has an interest in tennis and theclient's nationality is also determined (e.g., obtained from a profile,searching various social media platforms, entered into a database,requested directly, etc.). In this example, a tweet is an event andincludes information that Novak Djokovic commented that he needs a breakfrom tennis. In the example, the salesperson (e.g., a virtualrepresentative or real person) starts dialog regarding sports in generalas a topic node. The goal node topic in this case is a product/service“Watson.” The processing node 500 (FIG. 5) begins with the topic nodesfrom the customer, the salesperson, and the event, and obtains furtherevents that are related to the topic nodes (e.g., by searching networks,databases, etc., for other related current events to the topic nodes).The topic graph is generated and the suggestions are provided. The nextevent (news) is suggested as an upcoming Wimbledon tennis tournament intwo days. Following the last topic node, event (news) segues or bridgesthe last topic node with “Wimbledon is now using Watson technologies topredict winners.” The goal then is reached by providing the path to thegoal topic of Watson. As noted above, the suggestions or recommendationsmay be provided to a display (e.g., a display of a smart device,computing device, etc.) and the salesperson may be able to use thesuggestions during a dialog with the potential customer. Thesuggestions/recommendations may be inserted into a dialog stream betweena virtual representative (e.g., a virtual representative during a call,inserted into a webpage (e.g., a popup chat session), etc.).

FIGS. 11-15 are examples views 1100, 1200, 1300, 1400 and 1500 of addingweights on nodes for bridging topics to a goal topic, according to anembodiment. In the example in FIGS. 11-15, the weights 1110 have threevalues for frontier, N-label and distance label (D-label). In theexample shown in FIGS. 11-15, the weights are added to the nodes basedon the process that uses the breadth-first searches shown in FIGS. 8 and9. As shown, product Analytics and company, for example IBM, have zerovalues for frontier and N-label. In FIG. 12, the weights are filled infor frontier and N-label for Product, Company, U.S. Open 2016 andMurray. In FIG. 13, Event, Player, Federer, Djokovic and Tennis haveweights filled in for frontier and N-label. In FIG. 14, Client, JohnSmith, Interest, Sport and Wimbledon 2012 have weights filled in forfrontier and N-label. In FIG. 15 the topics, instance of topic and goalshave weights filled in for the distance label.

FIGS. 16A-B shows an example 1600 of interaction between a client 1610and a salesperson 1620 using a guided conversation (GC) app 1630 forbridging topics to a goal topic using bridges of a topic graph,according to an embodiment. In example 1600, the GC app is used in theuse-case for selling of a product. In this example 1600, the salesperson 1620 provides the GC app with a client name. The GC app searchesfor the client's 1610 interests, (A) gathers news on the topic of“tennis,” (B) gathers news for the topic “tennis” and “IBM,” determinesthe strongest path between A and B and generates a communication ofsegues to the sales person 1620 with a starting event. The communicationgenerated may be in the form of text messages to a smartphone orwearable device, speech (e.g., through an earbud the sales person 1620is wearing), etc. The sales person 1620 may then proceed to communicatewith the client 1610 using the segues received. The client 1610communicates back to the sales person 1620 with a new topic that isprovided to the GC app 1630.

In FIG. 16B, the GC app 1630 gathers (C) news on the new topics“Wimbledon 2012” and “Federer,” gathers (D) news on the topics “Murray”and “U.S. Open 2015,” gathers (E) news on topics “U.S. Open 2015” and“IBM” and determines an updated segue to send to the sales person 1620.The communication continues between the sales person 1620 and the client1610 and where the client 1610 shows interest, further analyticsexamples for U.S. Open players is then communicated to the client 1610.This example illustrates the use of obtaining information or segues forconducting a dialog that is steered towards a goal topic. In otherexamples, the sales person 1620 may be a virtual sales person that mayspeak over the telephone, through a kiosk, may provide text in a chatsession, etc.

FIG. 17 illustrates a block diagram for a process 1700 for topicbridging determination using topic graphs, according to one embodiment.In block 1710 process 1700 includes obtaining information includingprofile information and current event information. In block 1720, aprocessor (e.g., processor 510, FIG. 5) generates a topic graph byconverting the information to topic nodes in the topic graph. In block1730 the processor determines a weight assignment for each topic nodebased on ratios of sums of weights of edges from topic nodes. In block1740 process 1700 provides bridges from a given topic node to a neighborbased on the weight assignment.

In one embodiment, the bridges comprise a selected set of conversationaltopics or topic instances that bridge from an initial topic instance toa set of one or more topic instances. Process 1700 may further includemaintaining the topic graph based on individual profiles and the currentevent information, and maintaining weights on edges of the topic graphbased on one or more snippets from news or text that relate topicsconnected by an edge.

In one embodiment, process 1700 may further include ordering thesuggested paths based on the determined distance from one or more othertopic nodes to the topic goal node via an earliest topic node on eachsuggested path, and providing the suggested paths based on the ordering.In one embodiment, in process 1700 determining of the distance comprisesperforming a breadth-first search of the topic graph to determine edgecount distance from and reciprocal of a fraction of closer neighbornodes based on edge count to a topic goal node. Process 1700 may furtherinclude converting a text snippet of the current event information intoa set of one or more weighted edges between nodes in the topic graphthat represents topics found in the text snippet.

In one embodiment, process 1700 may include that edge count distance isreplaced by weighted edge count distance, the weights of edges beingdetermined by a relative number of co-occurrences in the one or moresnippets for current event or profile information of a pair of topicnodes connected by an edge. In one embodiment, the maintained weightsare determined based on popularity of the one or more snippets, and thecurrent event information comprises health event information for atleast one profile.

In one embodiment, process 1700 may include that the provided suggestedpaths are converted: for display on an electronic device or for speechvia a sound producing device, and that the converted suggested paths areprovided to a virtual representative for use in a dialog for driving thedialog to a topic of the topic goal node.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

References in the claims to an element in the singular is not intendedto mean “one and only” unless explicitly so stated, but rather “one ormore.” All structural and functional equivalents to the elements of theabove-described exemplary embodiment that are currently known or latercome to be known to those of ordinary skill in the art are intended tobe encompassed by the present claims. No claim element herein is to beconstrued under the provisions of 35 U.S.C. section 112, sixthparagraph, unless the element is expressly recited using the phrase“means for” or “step for.”

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A method comprising: obtaining informationincluding profile information and current event information; generating,by a processor, a topic graph by converting the information to topicnodes and edges in the topic graph; determining, by the processor, aweight assignment for each topic node based on ratios of sums of weightsof edges from topic nodes; performing a first search of the topic graphto determine edge count distance; performing a second search of thetopic graph to determine labeling each node visited with a derivedconversational distance to a topic goal node, wherein the second searchis a variant of a breadth-first search; providing bridges from a giventopic node to a neighbor node based on the weight assignment; convertingthe bridges to an electronic form of communication; and ordering, by theprocessor, suggested paths based on a determined distance from at leastone other topic node to the topic goal node via an earliest topic nodeon each suggested path, wherein the determined distance is found by thefirst search of the topic graph.
 2. The method of claim 1, wherein thebridges comprise a selected set of conversational topics or topicinstances that bridge from an initial topic instance to a set of one ormore topic instances, and the electronic form of communication is one ofa text message and virtual speech.
 3. The method of claim 1, furthercomprising: maintaining the topic graph based on individual profiles andthe current event information; and maintaining weights on edges of thetopic graph based on one or more snippets from news or text that relatetopics connected by an edge.
 4. The method of claim 3, furthercomprising: providing the suggested paths based on the ordering.
 5. Themethod of claim 3, further comprising: converting a text snippet of thecurrent event information into a set of one or more weighted edgesbetween nodes in the topic graph that represents topics found in thetext snippet.
 6. The method of claim 1, wherein the edge count distanceis replaced by a weighted edge count distance, the weights of edgesbeing determined by a relative number of co-occurrences in the one ormore snippets for current event or profile information of a pair oftopic nodes connected by an edge.
 7. The method of claim 6, wherein:maintaining weights on edges of the topic graph results in maintainedweights that are determined based on popularity of the one or moresnippets; and current event information comprises health eventinformation for at least one profile.
 8. The method of claim 4, wherein:the provided suggested paths are converted: for display on an electronicdevice or for speech via a sound producing device; and the convertedsuggested paths are provided to a virtual representative for use in adialog for driving the dialog to a topic of the topic goal node.
 9. Acomputer program product for topic bridging determination, the computerprogram product comprising a computer readable storage medium havingprogram instructions embodied therewith, the program instructionsexecutable by a processor to cause the processor to: obtain, by theprocessor, information data including profile information and currentevent information; generate, by the processor, a topic graph byconverting the information data to topic nodes in the topic graph;determine, by the processor, a weight assignment for each topic nodebased on ratios of sums of weights of edges from topic nodes; perform,by the processor, a first search of the topic graph to determine edgecount distance; perform, by the processor, a second search of the topicgraph to determine labeling each node visited with a derivedconversational distance to a topic goal node, wherein the second searchis a variant of a breadth-first search; provide, by the processor,bridges from a given topic node to a neighbor node based on the weightassignment; convert, by the processor, the bridges to an electronic formof communications; and order, by the processor, suggested paths based ona determined distance from at least one other topic node to the topicgoal node via an earliest topic node on each suggested path, wherein thedetermined distance is found by the first search of the topic graph. 10.The computer program product of claim 9, wherein the bridges comprise aselected set of conversational topics or topic instances that bridgefrom an initial topic instance to a set of one or more topic instances,and the electronic form of communication is one of a text message andvirtual speech.
 11. The computer program product of claim 9, furthercomprising program instructions executable by the processor to cause theprocessor to: maintain, by the processor, the topic graph based onindividual profiles and the current event information; and maintain, bythe processor, weights on edges of the topic graph based on one or moresnippets from news or text that relate topics connected by an edge. 12.The computer program product of claim 11, further comprising programinstructions executable by the processor to cause the processor to:provide, by the processor, the suggested paths based on the ordering.13. The computer program product of claim 11, further comprising programinstructions executable by the processor to cause the processor to:convert, by the processor, a text snippet of the current eventinformation into a set of one or more weighted edges between nodes inthe topic graph that represents topics found in the text snippet. 14.The computer program product of claim 9, wherein: the edge countdistance is replaced by weighted edge count distance, the weights ofedges being determined by a relative number of co-occurrences in the oneor more snippets for current event or profile information of a pair oftopic nodes connected by an edge; the maintained weights are determinedbased on popularity of the one or more snippets; and the current eventinformation comprises health event information for at least one profile.15. An apparatus comprising: a memory storing instructions; and aprocessor executing the instructions to: obtain information dataincluding profile information and current event information; generate atopic graph by converting the information data to topic nodes in thetopic graph; determine a weight assignment for each topic node based onratios of sums of weights of edges from topic nodes; perform a firstsearch of the topic graph to determine edge count distance; perform asecond search of the topic graph to determine labeling each node visitedwith a derived conversational distance to a topic goal node, wherein thesecond search is a variant of a breadth-first search; provide bridgesfrom a given topic node to a neighbor node based on the weightassignment; convert the bridges to an electronic form of communication;and order suggested paths based on a determined distance from at leastone other topic node to the topic goal node via an earliest topic nodeon each suggested path, wherein the determined distance is found by thefirst search of the topic graph.
 16. The apparatus of claim 15, whereinthe processor further executes instructions to: maintain the topic graphbased on individual profiles and the current event information; maintainweights on edges of the topic graph based on one or more snippets fromnews or text that relate topics connected by an edge; and provide thesuggested paths based on the ordering.
 17. The apparatus of claim 16,wherein the processor further executes instructions to: convert a textsnippet of the current event information into a set of one or moreweighted edges between nodes in the topic graph that represents topicsfound in the text snippet.
 18. The apparatus of claim 17, wherein: thecurrent event information comprises health event information for atleast one profile; the electronic form of communication is one of a textmessage and virtual speech; edge count distance is replaced by weightededge count distance, the weights of edges being determined by a relativenumber of co-occurrences in the one or more snippets for current eventor profile information of a pair of topic nodes connected by an edge;the maintained weights are determined based on popularity of the one ormore snippets; and the current event information comprises health eventinformation for the at least one profile.